fix

docs.it4i/software/bio/omics-master/overview.md

@@ -9,7 +9,7 @@ The scope of this OMICS MASTER solution is restricted to human genomics research
 The pipeline inputs the raw data produced by the sequencing machines and undergoes a processing procedure that consists on a quality control, the mapping and variant calling steps that result in a file containing the set of variants in the sample. From this point, the prioritization component or the diagnostic component can be launched.
 
 ![OMICS MASTER solution overview. Data is produced in the external labs and comes to IT4I (represented by the blue dashed line). The data pre-processor converts raw data into a list of variants and annotations for each sequenced patient. These lists files together with primary and secondary (alignment) data files are stored in IT4I sequence DB and uploaded to the discovery (candidate priorization) or diagnostic component where they can be analysed directly by the user that produced
-them, depending of the experimental design carried out.](../../img/fig1.png)
+them, depending of the experimental design carried out.](../../../img/fig1.png)
 
 Figure 1. OMICS MASTER solution overview. Data is produced in the external labs and comes to IT4I (represented by the blue dashed line). The data pre-processor converts raw data into a list of variants and annotations for each sequenced patient. These lists files together with primary and secondary (alignment) data files are stored in IT4I sequence DB and uploaded to the discovery (candidate prioritization) or diagnostic component where they can be analyzed directly by the user that produced them, depending of the experimental design carried out.
 
@@ -41,7 +41,7 @@ Output: FASTQ file plus an HTML file containing statistics on the data.
 
 FASTQ format It represents the nucleotide sequence and its corresponding quality scores.
 
-![FASTQ file.](../../img/fig2.png)
+![FASTQ file.](../../../img/fig2.png)
 Figure 2.FASTQ file.
 
 #### Mapping
@@ -81,7 +81,7 @@ corresponding information is unavailable.
 
 The standard CIGAR description of pairwise alignment defines three operations: ‘M’ for match/mismatch, ‘I’ for insertion compared with the reference and ‘D’ for deletion. The extended CIGAR proposed in SAM added four more operations: ‘N’ for skipped bases on the reference, ‘S’ for soft clipping, ‘H’ for hard clipping and ‘P’ for padding. These support splicing, clipping, multi-part and padded alignments. Figure 3 shows examples of CIGAR strings for different types of alignments.
 
-![SAM format file. The ‘@SQ’ line in the header section gives the order of reference sequences. Notably, r001 is the name of a read pair. According to FLAG 163 (=1+2+32+128), the read mapped to position 7 is the second read in the pair (128) and regarded as properly paired (1 + 2); its mate is mapped to 37 on the reverse strand (32). Read r002 has three soft-clipped (unaligned) bases. The coordinate shown in SAM is the position of the first aligned base. The CIGAR string for this alignment contains a P (padding) operation which correctly aligns the inserted sequences. Padding operations can be absent when an aligner does not support multiple sequence alignment. The last six bases of read r003 map to position 9, and the first five to position 29 on the reverse strand. The hard clipping operation H indicates that the clipped sequence is not present in the sequence field. The NM tag gives the number of mismatches. Read r004 is aligned across an intron, indicated by the N operation.](../../img/fig3.png)
+![SAM format file. The ‘@SQ’ line in the header section gives the order of reference sequences. Notably, r001 is the name of a read pair. According to FLAG 163 (=1+2+32+128), the read mapped to position 7 is the second read in the pair (128) and regarded as properly paired (1 + 2); its mate is mapped to 37 on the reverse strand (32). Read r002 has three soft-clipped (unaligned) bases. The coordinate shown in SAM is the position of the first aligned base. The CIGAR string for this alignment contains a P (padding) operation which correctly aligns the inserted sequences. Padding operations can be absent when an aligner does not support multiple sequence alignment. The last six bases of read r003 map to position 9, and the first five to position 29 on the reverse strand. The hard clipping operation H indicates that the clipped sequence is not present in the sequence field. The NM tag gives the number of mismatches. Read r004 is aligned across an intron, indicated by the N operation.](../../../img/fig3.png)
 
  Figure 3 . SAM format file. The ‘@SQ’ line in the header section gives the order of reference sequences. Notably, r001 is the name of a read pair. According to FLAG 163 (=1+2+32+128), the read mapped to position 7 is the second read in the pair (128) and regarded as properly paired (1 + 2); its mate is mapped to 37 on the reverse strand (32). Read r002 has three soft-clipped (unaligned) bases. The coordinate shown in SAM is the position of the first aligned base. The CIGAR string for this alignment contains a P (padding) operation which correctly aligns the inserted sequences. Padding operations can be absent when an aligner does not support multiple sequence alignment. The last six bases of read r003 map to position 9, and the first five to position 29 on the reverse strand. The hard clipping operation H indicates that the clipped sequence is not present in the sequence field. The NM tag gives the number of mismatches. Read r004 is aligned across an intron, indicated by the N operation.
 
@@ -123,8 +123,7 @@ VCF (3) is a standardized format for storing the most prevalent types of sequenc
 
 A VCF file consists of a header section and a data section. The header contains an arbitrary number of metainformation lines, each starting with characters ‘##’, and a TAB delimited field definition line, starting with a single ‘#’ character. The meta-information header lines provide a standardized description of tags and annotations used in the data section. The use of meta-information allows the information stored within a VCF file to be tailored to the dataset in question. It can be also used to provide information about the means of file creation, date of creation, version of the reference sequence, software used and any other information relevant to the history of the file. The field definition line names eight mandatory columns, corresponding to data columns representing the chromosome (CHROM), a 1-based position of the start of the variant (POS), unique identifiers of the variant (ID), the reference allele (REF), a comma separated list of alternate non-reference alleles (ALT), a phred-scaled quality score (QUAL), site filtering information (FILTER) and a semicolon separated list of additional, user extensible annotation (INFO). In addition, if samples are present in the file, the mandatory header columns are followed by a FORMAT column and an arbitrary number of sample IDs that define the samples included in the VCF file. The FORMAT column is used to define the information contained within each subsequent genotype column, which consists of a colon separated list of fields. For example, the FORMAT field GT:GQ:DP in the fourth data entry of Figure 1a indicates that the subsequent entries contain information regarding the genotype, genotype quality and read depth for each sample. All data lines are TAB delimited and the number of fields in each data line must match the number of fields in the header line. It is strongly recommended that all annotation tags used are declared in the VCF header section.
 
-![a) Example of valid VCF. The header lines ##fileformat and #CHROM are mandatory, the rest is optional but strongly recommended. Each line of the body describes variants present in the sampled population at one genomic position or region. All alternate alleles are listed in the ALT column and referenced from the genotype fields as 1-based indexes to
-this list; the reference haplotype is designated as 0. For multiploid data, the separator indicates whether the data are phased (|) or unphased (/). Thus, the two alleles C and G at the positions 2 and 5 in this figure occur on the same chromosome in SAMPLE1. The first data line shows an example of a deletion (present in SAMPLE1) and a replacement of two bases by another base (SAMPLE2); the second line shows a SNP and an insertion; the third a SNP; the fourth a large structural variant described by the annotation in the INFO column, the coordinate is that of the base before the variant. (b–f ) Alignments and VCF representations of different sequence variants: SNP, insertion, deletion, replacement, and a large deletion. The REF columns shows the reference bases replaced by the haplotype in the ALT column. The coordinate refers to the first reference base. (g) Users are advised to use simplest representation possible and lowest coordinate in cases where the position is ambiguous.](../../img/fig4.png)
+![a) Example of valid VCF. The header lines ##fileformat and #CHROM are mandatory, the rest is optional but strongly recommended. Each line of the body describes variants present in the sampled population at one genomic position or region. All alternate alleles are listed in the ALT column and referenced from the genotype fields as 1-based indexes to this list; the reference haplotype is designated as 0. For multiploid data, the separator indicates whether the data are phased (|) or unphased (/). Thus, the two alleles C and G at the positions 2 and 5 in this figure occur on the same chromosome in SAMPLE1. The first data line shows an example of a deletion (present in SAMPLE1) and a replacement of two bases by another base (SAMPLE2); the second line shows a SNP and an insertion; the third a SNP; the fourth a large structural variant described by the annotation in the INFO column, the coordinate is that of the base before the variant. (b–f ) Alignments and VCF representations of different sequence variants: SNP, insertion, deletion, replacement, and a large deletion. The REF columns shows the reference bases replaced by the haplotype in the ALT column. The coordinate refers to the first reference base. (g) Users are advised to use simplest representation possible and lowest coordinate in cases where the position is ambiguous.](../../../img/fig4.png)
 
  Figure 4 . (a) Example of valid VCF. The header lines ##fileformat and #CHROM are mandatory, the rest is optional but strongly recommended. Each line of the body describes variants present in the sampled population at one genomic position or region. All alternate alleles are listed in the ALT column and referenced from the genotype fields as 1-based indexes to this list; the reference haplotype is designated as 0. For multiploid data, the separator indicates whether the data are phased (|) or unphased (/). Thus, the two alleles C and G at the positions 2 and 5 in this figure occur on the same chromosome in SAMPLE1. The first data line shows an example of a deletion (present in SAMPLE1) and a replacement of two bases by another base (SAMPLE2); the second line shows a SNP and an insertion; the third a SNP; the fourth a large structural variant described by the annotation in the INFO column, the coordinate is that of the base before the variant. (b–f ) Alignments and VCF representations of different sequence variants: SNP, insertion, deletion, replacement, and a large deletion. The REF columns shows the reference bases replaced by the haplotype in the ALT column. The coordinate refers to the first reference base. (g) Users are advised to use simplest representation possible and lowest coordinate in cases where the position is ambiguous.
 
@@ -341,19 +340,19 @@ The output folder contains all the subfolders with the intermediate data. This f
 
 Once the file has been uploaded, a panel must be chosen from the Panel list. Then, pressing the Run button the diagnostic process starts. TEAM searches first for known diagnostic mutation(s) taken from four databases: HGMD-public (20), [HUMSAVAR](http://www.uniprot.org/docs/humsavar), ClinVar (29) and COSMIC (23).
 
-![The panel manager. The elements used to define a panel are (A) disease terms, (B) diagnostic mutations and (C) genes. Arrows represent actions that can be taken in the panel manager. Panels can be defined by using the known mutations and genes of a particular disease. This can be done by dragging them to the Primary Diagnostic box (action D). This action, in addition to defining the diseases in the Primary Diagnostic box, automatically adds the corresponding genes to the Genes box. The panels can be customized by adding new genes (action F) or removing undesired genes (action G). New disease mutations can be added independently or associated to an already existing disease term (action E). Disease terms can be removed by simply dragging themback (action H).](../../img/fig7x.png)
+![The panel manager. The elements used to define a panel are (A) disease terms, (B) diagnostic mutations and (C) genes. Arrows represent actions that can be taken in the panel manager. Panels can be defined by using the known mutations and genes of a particular disease. This can be done by dragging them to the Primary Diagnostic box (action D). This action, in addition to defining the diseases in the Primary Diagnostic box, automatically adds the corresponding genes to the Genes box. The panels can be customized by adding new genes (action F) or removing undesired genes (action G). New disease mutations can be added independently or associated to an already existing disease term (action E). Disease terms can be removed by simply dragging themback (action H).](../../../img/fig7x.png)
 
  Figure 7. The panel manager. The elements used to define a panel are ( A ) disease terms, ( B ) diagnostic mutations and ( C ) genes. Arrows represent actions that can be taken in the panel manager. Panels can be defined by using the known mutations and genes of a particular disease. This can be done by dragging them to the Primary Diagnostic box (action D ). This action, in addition to defining the diseases in the Primary Diagnostic box, automatically adds the corresponding genes to the Genes box. The panels can be customized by adding new genes (action F ) or removing undesired genes (action G). New disease mutations can be added independently or associated to an already existing disease term (action E ). Disease terms can be removed by simply dragging them back (action H ).
 
 For variant discovering/filtering we should upload the VCF file into BierApp by using the following form:
 
-![BierApp VCF upload panel. It is recommended to choose a name for the job as well as a description.](../../img/fig8.png)\
+![BierApp VCF upload panel. It is recommended to choose a name for the job as well as a description.](../../../img/fig8.png)\
 
  Figure 8 . \BierApp VCF upload panel. It is recommended to choose a name for the job as well as a description \\.
 
 Each prioritization (‘job’) has three associated screens that facilitate the filtering steps. The first one, the ‘Summary’ tab, displays a statistic of the data set analyzed, containing the samples analyzed, the number and types of variants found and its distribution according to consequence types. The second screen, in the ‘Variants and effect’ tab, is the actual filtering tool, and the third one, the ‘Genome view’ tab, offers a representation of the selected variants within the genomic context provided by an embedded version of the Genome Maps Tool (30).
 
-![This picture shows all the information associated to the variants. If a variant has an associated phenotype we could see it in the last column. In this case, the variant 7:132481242 CT is associated to the phenotype: large intestine tumor.](../../img/fig9.png)
+![This picture shows all the information associated to the variants. If a variant has an associated phenotype we could see it in the last column. In this case, the variant 7:132481242 CT is associated to the phenotype: large intestine tumor.](../../../img/fig9.png)
 
  Figure 9 . This picture shows all the information associated to the variants. If a variant has an associated phenotype we could see it in the last column. In this case, the variant 7:132481242 CT is associated to the phenotype: large intestine tumor.
 

docs.it4i/software/chemistry/phono3py.md

@@ -61,7 +61,7 @@ POSCAR-00006   POSCAR-00015 POSCAR-00024 POSCAR-00033 POSCAR-00042 POSCAR-00051
 POSCAR-00007   POSCAR-00016 POSCAR-00025 POSCAR-00034 POSCAR-00043 POSCAR-00052 POSCAR-00061 POSCAR-00070 POSCAR-00079 POSCAR-00088 POSCAR-00097 POSCAR-00106
 ```
 
-For each displacement the forces needs to be calculated, i.e. in form of the output file of VASP (vasprun.xml). For a single VASP calculations one needs [KPOINTS](software/chemistry/KPOINTS), [POTCAR](software/chemistry/POTCAR), [INCAR](software/chemistry/INCAR) in your case directory (where you have POSCARS) and those 111 displacements calculations can be generated by [prepare.sh](software/chemistry/prepare.sh) script. Then each of the single 111 calculations is submitted [run.sh](software/chemistry/run.sh) by [submit.sh](software/chemistry/submit.sh).
+For each displacement the forces needs to be calculated, i.e. in form of the output file of VASP (vasprun.xml). For a single VASP calculations one needs [KPOINTS](KPOINTS), [POTCAR](POTCAR), [INCAR](INCAR) in your case directory (where you have POSCARS) and those 111 displacements calculations can be generated by [prepare.sh](prepare.sh) script. Then each of the single 111 calculations is submitted [run.sh](run.sh) by [submit.sh](submit.sh).
 
 ```console
 $./prepare.sh

docs.it4i/software/debuggers/allinea-performance-reports.md

@@ -56,4 +56,4 @@ Now lets profile the code:
 $ perf-report mpirun ./mympiprog.x
 ```
 
-Performance report files [mympiprog_32p\*.txt](software/debuggers/mympiprog_32p_2014-10-15_16-56.txt) and [mympiprog_32p\*.html](software/debuggers/mympiprog_32p_2014-10-15_16-56.html) were created. We can see that the code is very efficient on MPI and is CPU bounded.
+Performance report files [mympiprog_32p\*.txt](mympiprog_32p_2014-10-15_16-56.txt) and [mympiprog_32p\*.html](mympiprog_32p_2014-10-15_16-56.html) were created. We can see that the code is very efficient on MPI and is CPU bounded.

docs.it4i/software/intel/intel-suite/intel-trace-analyzer-and-collector.md

@@ -30,7 +30,7 @@ $ traceanalyzer
 
 The GUI will launch and you can open the produced `*`.stf file.
 
-![](../../img/Snmekobrazovky20151204v15.35.12.png)
+![](../../../img/Snmekobrazovky20151204v15.35.12.png)
 
 Please refer to Intel documenation about usage of the GUI tool.
 

docs.it4i/software/mpi/ompi-examples.md

@@ -178,7 +178,7 @@ class Hello {
 }
 ```
 
-* C:                   [hello_c.c](../../../src/ompi/hello_c.c)
+* C:                   [hello_c.c](../../src/ompi/hello_c.c)
 * C++:                 [hello_cxx.cc](../../src/ompi/hello_cxx.cc)
 * Fortran mpif.h:      [hello_mpifh.f](../../src/ompi/hello_mpifh.f)
 * Fortran use mpi:     [hello_usempi.f90](../../src/ompi/hello_usempi.f90)
@@ -202,11 +202,11 @@ Additionally, there's one further example application, but this one only uses th
 
 ### Test the Connectivity Between All Pross
 
-* C:   [connectivity_c.c](src/ompi/connectivity_c.c)
+* C:   [connectivity_c.c](../../src/ompi/connectivity_c.c)
 
 ## Build Examples
 
-Download [examples](src/ompi/ompi.tar.gz).
+Download [examples](../../src/ompi/ompi.tar.gz).
 
 The Makefile in this directory will build the examples for the supported languages (e.g., if you do not have the Fortran "use mpi" bindings compiled as part of OpenMPI, those examples will be skipped).
 

docs.it4i/software/tools/ansys/workbench.md

@@ -4,7 +4,7 @@
 
 It is possible to run Workbench scripts in batch mode. You need to configure solvers of individual components to run in parallel mode. Open your project in Workbench. Then, for example, in Mechanical, go to Tools - Solve Process Settings ...
 
-![](../../img/AMsetPar1.png)
+![](../../../img/AMsetPar1.png)
 
 Enable Distribute Solution checkbox and enter number of cores (e.g. 48 to run on two Salomon nodes). If you want the job to run on more then 1 node, you must also provide a so called MPI appfile. In the Additional Command Line Arguments input field, enter: